Active thermography is used to nondestructively evaluate samples for sub-surface defects. It is effective for uncovering internal bond discontinuities, delaminations, voids, inclusions, and other structural defects that are not detectable by visual inspection of the sample. Generally, active thermography involves heating or cooling the sample to create a difference between the sample temperature and the ambient temperature and then observing the infrared thermal signature that emanates from the sample as its temperature returns to ambient temperature. An infrared camera is used because it is capable of detecting any anomalies in the cooling behavior, which would be caused by sub-surface defects blocking the diffusion of heat from the sample surface to the sample's interior. More particularly, these defects cause the surface immediately above the defect to cool at a different rate that the surrounding defect-free areas.
As the sample cools, the infrared camera monitors and records an image time sequence indicating the surface temperature, thereby creating a record of the changes in the surface temperature over time. It is the current practice to use a human operator to view the record of these changes and to look for “hot spots” in the image record. In many instances, this analysis is purely visual (i.e. the human inspector views a display of the image output on a monitor and identifies regions that appear “hot” compared to surrounding areas. More sophisticated methods attempt to use numerical processing of the data by generating contrast curves relative to a reference specimen of known quality and composition (a so-called “gold standard”). This reference specimen, which is known to be defect free, is typically placed in the field of view of the imaging camera. In other instances, the “gold standard” is not a reference specimen at all, but rather it is an image that has been derived from a physical model. However, in general, the time history of the cooling of the sample is not viewed as a whole (i.e. a contiguous sequence), but rather as a collection of individual frames acquired from the infrared camera. These methods work adequately for large, or near surface, defects. However, as manufacturing processes and safety standards requirements place higher demands regarding smaller/more subtle defect detection, these traditional methods become less effective because the small signal levels associated with subtle defects are lost in the noise, drift, and instability that is inherent to infrared cameras. Also, visual defect identification methods tend to be subjective, and they do not readily and easily lend themselves to the automatic defect detection process. Further, it is not possible to measure the depth of the defects simply by viewing the infrared images.
There have been attempts to determine the depth of a defect via processing and analysis of the data from the infrared camera and also to automate the defect detection process. In some cases, the data from the infrared camera is transferred to a computer for processing and analysis to detect variations in the cooling behavior or to perform mathematical operations on the data to determine the depth of the sub-surface defect or other defect properties. These types of calculations, however, often require expensive low noise, high-speed digital infrared cameras. Further, the cumbersome nature of having a computer attached to the camera for conducting calculations makes the combination impractical for applications outside of a laboratory, such as field inspections.
Also, infrared data sequences of thermal decay typically used in non-destructive testing tend to be difficult to manipulate mathematically due to their low signal-to-noise ratios and large dynamic range and also require a great deal of computer processing power, memory and storage space.
One attempt at automating the defect detection process involves analyzing the contrast between each pixel in the image and a reference to generate a curve representing the amount of contrast between each pixel and the reference. The reference can be established any number of ways including using a reference pixel (from the sample image), a pixel group (from the sample image). If a pixel, or a pixel group is used, a reference point or reference area of the sample must be defined. The reference can be a defect-free area of the sample, or the mean of the entire field of view of the camera (when viewing the sample). The temperature-time history of this reference pixel or pixel group is subtracted from the time history of each pixel in the image to generate a contrast vs. time plot. Any significant temperature difference between any given pixel and the reference indicates the presence of a defect which will exhibit itself as a peak in the contrast vs. time plot. The contrast vs. time plot can be measured with respect to the time at which the peak occurs, the time at which a maximum ascending slope occurs, and/or a moment of the curve for each pixel. Other options, such as generating and displaying the contrast vs. time plot with a reference plot and checking the point at which the two plots separate, have also been applied.
Such contrast-based methods tend to have significant shortcomings, however. In addition to the data storage, memory and processing problems noted above due to the large size of the infrared image data files, contrast-based methods require the identification of a defect-free region on the sample as a reference point. This requirement is often not realistic for some samples if, for example, the size of the defect is larger than the infrared camera's field of view. In such a case, there is no defect-free area available that can act as a reference for a given region. Further, if the entire sample exhibits a defect (e.g., a large delamination running underneath the entire surface of the sample), there is no contrast between any region of the sample because the whole sample is equally or nearly equally defective.
Contrast-based methods that rely on the mean of the entire field of view as a reference have also been used, but this method assumes that the defect area in the field is small enough so that it will not appreciably influence the mean. If a defect (or group of defects) occupies a large portion of the field of view, the contrast method is ineffective because a significant portion of the mean value result is composed of data derived from defective sample points which acts to reduce any appreciable difference between the defect area and the mean when the contrast value is calculated.
Regardless of the specific reference value used in detecting defects, the results obtained using contrast-based methods depend strongly on the choice of reference region on the sample. More particularly, the results obtained in contrast-based methods can be altered by simply changing the location of the reference region.
Further, in evaluating the results from both the contrast-based methods and the data obtained directly from the infrared camera, identifying the time at which a maximum peak slope occurs (indicating the presence of a defect) is often difficult because the signals are often inherently noisy, thus the contrast based method must be capable of discriminating between pixels associated with defects and pixels associated with noise. Although the peak slope (of the temperature vs. time relationship) is a useful indicator of defect depth, the peak slope inherently must occur earlier than the peak contrast and may be obscured by the heating event, or by lingering heat from the equipment after flash heating the sample. The peak slope may also be obscured if the instantaneous temperature of the sample exceeds the camera's peak temperature detection capabilities, causing an initial, highly nonlinear response from the camera due to camera saturation.
A common approach to improving the signal-to-noise content of thermographic data is to replace the amplitude of each pixel with the mean or median value of that pixel and its surrounding nearest neighboring pixels as defined by an N×N matrix, where N is a selected integer. This approach, however, sacrifices spatial resolution to lessen temporal noise. Another approach for reducing temporal noise is to average data over a selected number of consecutive frames, but this approach sacrifices temporal precision. As a result, known techniques for reducing temporal and spatial noise necessarily degrade temporal and/or spatial resolution and precision.
Another technique which may be used in attempt to filter noise from thermographic data is to simply fit the raw temperature-time history of each data point of the sample, with a polynomial or a set of orthogonal functions. However, when one understands the underlying physical process of thermal imaging as well as the nuances of using all but the most expensive thermal imaging cameras, these approaches prove unsuccessful for several reasons:
A. Thermographic data (when generated using a pulse of energy to heat the sample), presents an extremely large dynamic range thereby making it extremely difficult to accurately fit both the data occurring early in the sampling process (large amplitude) and later in the sampling process (small amplitude). Specifically, the very steep, early post-excitation behavior of the temperature-time history of a point requires a high order polynomial or other similar expansion to accurately model the data. However, such high order terms introduce undesirable errors (such as oscillations) in the polynomial fit later in the time-temperature sequence when in fact the data is not oscillatory but rather stable.
B. The early, high amplitude response of the sample's thermal signature, is often outside, or near the calibration limit of the infrared camera. As a result, the signal generated during this portion of the imaging exercise is often highly non-linear.
C. The early, high amplitude, data points dominate the fit and worsen the fit for later occurring data points.
The difference between the thermal response of an intact point (i.e. a defect free portion of a sample) and the thermal response of a sub-surface defects is often very small (e.g. on the order of the temperal noise) and accordingly very difficult to identify.
Thermographic Fingerprinting
In many manufacturing applications, determining whether a component is within specifications is critical. In many such applications, the particular details of the defect are not critical and a simple “pass/no pass” test is sufficient. A defective component may fail to be “in spec” not because of a discreet defect, but rather because of distributed defects caused by process variables or problems due to tooling or material composition. In the vast majority of manufacturing applications, it is highly desirous to automate inspections from both a cost standpoint and an accuracy standpoint. Many quality assurance schemes accomplish the “pass/no pass” test by comparing a production specimen to that of a defect free specimen, also known as a “gold standard” specimen.
When using “gold standard” testing in the context of thermal imaging, a defect free specimen is created or chosen having internal features, which meet all manufacturing specifications (such as depth and size of apertures or bonds and joints of known acceptable quality, etc.). Once a “gold standard” specimen is selected, its thermal emission pattern is captured and stored. Thereafter the thermal data from the “gold standard” is compared to the thermal emission data associated with the production specimen. If the thermal emission patterns of the two specimens deviate from one another more than a predetermined amount, the production specimen is deemed defective. This comparison between the “gold standard” and the production specimen can be conducted by a human operator, or it can be carried out automatically using various software routines that compare the thermal emission data from the two specimens. Although the advantages associated with automated inspection are obvious, there are various challenges posed by automated inspection of thermal images. For example, most software algorithms used to compare the thermal image of the “gold standard” against the thermal image of a production specimen require pixel-to-pixel registration between the two data groups created from the thermal emission data of the two specimens. Thus, these software routines will render inaccurate results if the orientation (rotation or translation) of the production image differs from that of the “gold standard” image. Additionally, many known software algorithms employ temperature based schemes which makes them highly sensitive to the camera angle of the thermal imaging camera and the amount of heat energy used to thermally stimulate the samples.
At this time there are essentially two approaches used in employing pulsed thermography in non-destructive testing applications. The first approach is to construct thermographic images wherein each image represents particular time frame in the cooling sequence. These time frames are analyzed in an attempt to identify points or regions where anomalous local contrast exists. The images are typically constructed from image data that has undergone of one or more preprocessing operations. Although this approach is effective in some applications, it has several notable drawbacks. Three of which are as follows:                1. You can only examine discrete “slices” in each image and, by definition, a single “slice” does not describe the state of the entire sample.        2. The success of the method is based on the presence of localized defects that fall within the field of view of the imaging apparatus. Accordingly, this approach does not lend itself to discriminate between a sample that is “all good” or “all bad” even though it is often highly desirable to be able to discriminate in this way.        3. Comparison of two samples can only be accomplished by image-to-image comparison of each image “slice” in the corresponding data sequence of each sample. This approach is not only cumbersome but lends itself to inaccuracies (if the images contain temperature or temperature contrast data). These inaccuracies arise primarily because it is very difficult to establish repeatable temperatures from shot-to-shot (inasmuch as temperature is highly dependent on ambient conditions, heat energy input, energy distribution and other factors).        
The second pulsed thermography approach is one where a sample is imaged and the imaged data is analyzed on a pixel-by-pixel basis in order to measure some physical quantity (such as sample wall thickness, defect depth, thermal diffusivity, or the like). This approach reduces the sequence of images into a single image representing thickness, depth, diffusivity or the like, a based on a time characteristic measured in each pixel's time history. Although this type of information is very useful in terms of physical dimensions or determining whether or not there are defects in the sample, it is entirely possible that a sample could be defect-free and yet still be sufficiently deficient to warrant rejection. For example, a specimen could be compositionally different (e.g. a mixture in the fabrication process was incorrect), or it could contain an excessive amount of porosity (pores that are too small to be resolved thermally, but that effect the density of the sample). Such samples could be easily “passed” using a pixel-by-pixel analysis approach.
Unlike the two above-referenced approaches, the approach disclosed herein allows visualization of the entire time sequence of the entire sample. Even if no “defect” or dimensional change occurs, subtle changes in the sample (or sample composition) will cause corresponding changes in the shape of the “fingerprint” image. The changes that occur may be too subtle to be detected by prior art methods but, using “fingerprint” the method of the present invention, they are made apparent when directly correlated against a gold standard “fingerprint.”